METHOD OF DIAGNOSING AUTISM SPECTRUM DISORDER

Description

FIELD OF THE INVENTION

The present invention relates to the diagnosis of Autism spectrum disorder (ASD), or predisposition to develop ASD. In particular it relates to a method for diagnosing an ASD, or predisposition to develop an ASD, by investigating a set of single nucleotide polymorphisms (SNPs) in a sample from a subject.

BACKGROUND TO THE INVENTION

Autism Spectrum Disorders (ASDs) are a spectrum of neurodevelopmental conditions characterized by impairments in social interaction and communication, and associated with repetitive, restricted patterns of interest or behaviour. Autism Spectrum Disorders is an umbrella term used to describe a number of autism disorders such as classic autism, Asperger's Syndrome, atypical autism and pervassive developmental disorder not otherwise specified.

ASDs are relatively common neurodevelopmental disorders, affecting approximately 1% of the population. Autism shows a well established gender distortion with about four times as many males as females being affected.

Currently a diagnosis of ASD is formulated using behavioural criteria, with the aid of diagnostic manuals, for example the International Classification of Disease (ICD-10) and Diagnostic and Statistic Manual of mental health (DSM-IV). The diagnosis of autism is not unified and a number of distinct criteria are applied in different parts of the world. In many European countries diagnostic criteria like DSM-IV for psychiatric diseases are applied. The Autism Diagnostic Interview (ADI) and Autism Diagnostic Observation Schedule (ADOS) are diagnostic tests, and have become a kind of ‘gold standard’ and are increasingly being implemented in both the USA and Europe.

The Autism Diagnostic Interview-Revised (ADI-R) is a diagnostic assessment for ASD. It is a parental interview that probes for language, social, behavioural, and functional abnormalities that are inconsistent with a specific child's stage of development. The ADI-R is a standardized, semi-structured clinical review for caregivers of children and adults. The interview contains 111 items and focuses on behaviours in three content areas: quality of social interaction, (e.g., emotional sharing, offering and seeking comfort, social smiling and responding to other children); communication and language (e.g., stereotyped utterances, pronoun reversal, social usage of language); and repetitive, restricted and stereotyped interests and behaviour (e.g., unusual preoccupations, hand and finger mannerisms, unusual sensory interests). Responses are scored by the clinician based on the caregiver's description of the child's behaviour. This interviewer-based instrument requires substantial training in administration and scoring, making it very time-consuming and expensive. As diagnosis also depends on the assessment of both the caregiver and interviewer, it is also highly subjective.

The main treatment proposed for ASDs are based on intensive educational programs, but also include pharmacotherapy and cognitive behavioural approaches. Sustained special education programs and behavior therapy early in life can help children acquire self-care, social, and job skills. Available approaches include applied behavior analysis (ABA), developmental models, structured teaching, speech and language therapy, social skills therapy, and occupational therapy. Applied early enough, studies have shown that as many as 50% of autistic children participating in such programs can be referred back to normal schooling and education. In a recent UK study the potential socio-economic benefit of early intensive treatment has been estimated to be as high as £1.8 million per patient over the life-time of the patient.

However, the age at which the therapy is provided and started is of significant importance. Ideally, it is thought that the programs should start at 18 months age, at the latest.

As outlined above, the ADI-R cannot be used for diagnosis under the age of 18 months. Indeed, for infra-structural (availability of trained experts, in the US only 10% of suspected autistic children have direct access to specialists able to carry out ADI-R) and social reasons the average age of diagnosis is 5 years in the US and 8 years in some parts of Europe.

Of importance also is that there is increasing recognition that many adults with ASD have not been recognised or diagnosed. In adults, however, current symptoms are often modulated by coping strategies developed over the life-span, and retrospective accounts of past symptoms rely not only on the availability of an informant but also on their reliability. Moreover, for reasons of confidentiality, many adults do not wish others to be interviewed about their condition—and so a full diagnostic developmental history cannot be obtained to allow a confident diagnosis of ASD.

Hence there is a clear need for improved diagnostic methods for ASDs which address the problems associated with the behavioural characterisation studies currently in use. In particular, there is a need for an early-detection method enabling early intervention, which is thought to be essential in order to have a significant impact on the child's development.

DESCRIPTION OF THE FIGURES

FIG. 1—A graph to show the relationship between the number of SNPs used in the diagnostic assay and the overall accuracy of ASD affected/unaffected classification.

SUMMARY OF THE INVENTION

The present inventors have developed a new genetic test which can diagnose ASD with over 96% accuracy. The advantage of a genetic test is that it can be conducted at any age, for example at birth or during babyhood, allowing appropriate educational programmes to be started in early infancy and maximal benefit to be gained from such programs. It can also be applied in adulthood. A genetic test also does away with the need for trained professional to carry out behavioural testing, and addresses the problems associated with inconsistencies between the different behavioural tests and subjectivity of the caregiver/interviewer.

Thus, in a first aspect, the present invention provides a method for diagnosing an autism spectrum disorder (ASD), or predisposition to develop an ASD, in a subject, which comprises the step of investigating a set of single nucleotide polymorphisms (SNPs) in a sample from the subject, wherein the number of SNPs in the set is such that the method can diagnose ASD with at least 70% accuracy.

The set of SNPs may be at least partly derivable from the list of SNPs given in Table 3. For example, the set may comprise at least 1500 SNPs, at least 2300 SNPs or all 3126 SNPs from the list given in Table 3.

The set of SNPs may comprise at least 70% of the SNPs weighted at least ±0.01 in Table 3.

The set of SNPs may comprise between 1500 and 4500 SNPs, between 2300 and 3900 SNPs, or between 3000 and 3300 SNPs.

The set of SNPs may comprise one or more SNPs which are highly correlated with one or more SNPs from the list given in Table 3.

In a second aspect, the present invention provides a kit for diagnosing an autism spectrum disorder (ASD), or predisposition to develop an ASD, in a subject, which comprises a plurality of primer pairs or probes capable of investigating a set of single nucleotide polymorphisms (SNPs) in a sample from the subject, wherein the set of SNPs is as defined in accordance with the first aspect of the invention.

Where the kit comprises a plurality of probes, they may be immobilised on a solid support.

In a third aspect, the present invention provides a method for preparing a kit according to the second aspect of the invention which comprises the step of immobilising the plurality of probes on to a solid support.

DETAILED DESCRIPTION

Autism Spectrum Disorder

Autism spectrum disorders (ASD) are developmental disorders resulting from dysfunction in the central nervous system and are characterized by impairments in three behavioural areas: communication (including spoken language), social interactions, and repetitive behaviours or restricted interests. ASDs usually manifest before three years of age and the severity can vary greatly. Idiopathic ASDs currently include autism, which is considered to be the most severe form; pervasive developmental disorders not otherwise specified (PDD-NOS); and Asperger's syndrome, a form of autism in which persons can have relatively normal intelligence and communication skills but difficulty with social interactions.

ASD may be diagnosed using behavioural criteria, with the aid of diagnostic manuals, for example the International Classification of Disease (ICD-10) and Diagnostic and Statistic Manual of mental health (DSM-IV); or by using Autism Diagnostic Interview-Revised (ADI-R) which is a diagnostic assessment for ASD.

The method of the present invention may be capable of diagnosing an autism spectrum disorder (ASD), for example it may be used to establish or confirm that a subject is affected by an ASD. In this embodiment, the subject may already show symptoms of the ASD, such as impaired social interaction and/or communication, or repetitive patterns of interest or behaviour.

The method of the present invention may be capable of diagnosing or detecting a predisposition to develop an ASD. For example, it may be used to predict the likelihood that a subject will develop an ASD, maybe before the subject shows one or more symptom(s) of an ASD. This embodiment is particularly useful for the evaluating the likelihood of ASD development in a subject too young for ASD examination using classical behavioural analysis, such as a subject less that 18 months old. Also it will allow diagnosis in people (e.g. adults or refugees) who have no informants available to confirm their developmental history.

Single Nucleotide Polymorphisms

As used herein, single-nucleotide polymorphism (SNP) is a DNA sequence variation occurring when a single nucleotide (A, T, C, or G) in the genome differs between an individual affected with an ASD and an unaffected individual.

Single nucleotides may be changed (substitution), removed (deletions) or added (insertion) to a polynucleotide sequence. Insertion or deletion SNPs (InDels) may shift translational frame.

Single nucleotide polymorphisms may fall within coding sequences of genes, non-coding regions of genes, or in the intergenic regions between genes.

The method of the invention involves investigating a set of single nucleotide polymorphisms (SNPs) in a sample from the subject. In other words, the presence of absence of a plurality of SNPs in a subject is analysed, in order to give an overall “score” from which it can be deduced whether a subject has, or is likely to develop a predisposition to ASD.

SNPs may be defined by their position within the genome, for example as an “rs” number (see Table 3). Relevant sequence information may be found from public databases such as http://genome.uscs.edu or http://www.ncbi.nlm.nih.gov/snp.

SNP Sets

Using the Autism Genetic Resource Exchange (AGRE) sample (see below), comprising 1385 individuals with ASD and 1494 unaffected individuals, a whole genome association study identified 390671 SNPs.

Subsequent analysis revealed that accuracy could be improved by using a sub-set of this total number, including the most “influential” SNPs (i.e. those with the highest weighting). A SNP set consisting of the 3126 SNPs listed in Table 3 gave an overall ASD classification accuracy of over 96.6%.

It is likely that small modifications to the number of SNPs in the set can be made without significantly affecting accuracy. For example, the nine SNPs from Table 3 shown below:

• • rs7965985
  • rs753213
  • rs7403957
  • rs4698515
  • rs3923686
  • rs6908859
  • rs7794971
  • rs793091
  • SNP_A-1992337
    are listed with a weighting of “0”, so it is likely that any or all of these could be removed without affecting classification accuracy.

It is believed, however that larger increases or decreases in the number of SNPs in the set will decrease accuracy, but this may still be within acceptable levels for a diagnostic test.

For example, as shown in Table 2, a SNP set comprising 2345 SNPs achieved an accuracy of 89% and a SNP set comprising 3907 SNPs achieved an accuracy of 89.09%.

In accordance with the present invention, the number of SNPs in the SNP set should be such that the accuracy of ASD classification is at least 70%.

The SNP set may comprise at least 1500, at least 2300, or at least 3000 SNPs from the list given in Table 3.

The SNP set may comprises substantially all 3126 SNPs given in Table 3. For example, the SNP set may comprise between 3110 and 3126 SNPs from the list given in Table 3.

The SNP set may comprises at least 70%, 80%, 90% or 95% of the SNPs weighted at least ±0.01 in Table 3.

The SNP set may comprises at least 70%, 80%, 90% or 95% of the SNPs weighted at least ±0.02 in Table 3.

The SNP set may comprises at least 70%, 80%, 90% or 95% of the SNPs weighted at least ±0.03 in Table 3.

The SNP set may comprises at least 70%, 80%, 90% or 95% of the SNPs weighted at least ±0.04 in Table 3.

The SNP set may comprise, for example, between 1500 and 4500, between 2300 and 3900 SNPs, or between 3000 and 3300 SNPs.

Highly Correlated SNPS

The SNP set may comprise on or more SNPs which are highly correlated with one or more SNPs from the list given in Table 3 Linkage disequilibrium (LD) is the measure of how correlated one SNP is to another. Within the list of SNPs given in Table 3, a person skilled in the art can calculate SNPs which will be in high correlation (LD) with those SNPs, which in turn may be predictive for ASDs.

LD provides a score (r2) ranging from 0-1. A highly correlated SNP may have a score of at least 0.7, 0.8 or 0.9 based on the set of SNPs given in Table 3.

Accuracy

The level of accuracy obtained using a given SNP set may be obtained by challenging the SNP to diagnose ASD for a group of individuals whose ASD status is already known, for example by standard behavioural classification.

The % accuracy for a given SNP set may be obtained by:

number of correctly classified individuals/number of individuals×100

The group of individuals may comprise ASD affected subjects, ASD unaffected subjects or a combination of both types of subject. Where the group comprises both ASD affected and ASD unaffected subjects, the SNP set is challenged for its capacity to identify individuals both “positively” and “negatively”, providing a more robust result.

The group of individuals should be large enough to ensure that the calculated accuracy levels are statistically significant. Too small a test group may not provide a complete picture of the significance of a given result, whether it is a positive or negative correct classification or a “false positive” or “false negative”.

The test group may, for example, comprise at least 100, 500 or 1000 individuals.

The test group may be Autism Genetic Resource Exchange sample, as described in the Examples.

It is also possible to measure both specificity and sensitivity as follows:

Specificity: True negatives/True negatives+False positives
Sensitivity: True positives/True positives+True negatives

The specificity of the SNP set may be at least 70%, at least 80%, at least 85% or at least 90%.

The sensitivity of the SNP set may be at least 70%, at least 80%, at least 85% or at least 90%.

SNP Investigation

The term “investigation” is used to mean that the presence or absence of a SNP in a given genome is determined.

Applicable diagnostic techniques include, but are not limited to, DNA sequencing including mini-sequencing, primer extension, hybridization with allele-specific oligonucleotides (ASO), oligonucleotide ligation assays (OLA), PCR using allele-specific primers (ARMS), dot blot analysis, flap probe cleavage approaches, restriction fragment length polymorphism (RFLP), kinetic PCR, and PCR-SSCP, fluorescent in situ hybridisation (FISH), pulsed field gel electrophoresis (PFGE) analysis, Southern blot analysis, single stranded conformation analysis (SSCA), denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), denaturing HPLC (DHPLC), and RNAse protection assays, all of which are known to the person skilled in the art.

For a known SNP, direct determination of the respective genotype is usually the method of choice. State of the art approaches for industrial high-throughput genotyping today rely on one of four different mechanisms: allele-specific primer extension, allele-specific hybridization, allele-specific oligonucleotide ligation and allele-specific cleavage of a flap probe (K wok, Pharmacogenomics 1, 95 (2000)). Sequencing or mini-sequencing protocols are part of the primer extension methods, e.g. genomic DNA sequencing, either manual or by automated means. Minisequencing (primer extension) technology is based on determining the sequence at a specific base by allowing the elongation of a primer by one base directly at the variant site (Landegren et al., Genome Res. 8: 769-76 (1998)). Short sequence reactions coupled with an alternative detection method are the nature of real time pyrophosphate sequencing (Nyren et al., Science 281:363 (1998)).

Allele-specific hybridization protocols rely on probes detecting one or several of the alleles present at the SNP positions. Several techniques were developed for detection of a hybridization event. In the 5′ nuclease assay and in the molecular beacon assay, the hybridization probes are fluorescently labelled and probe binding is detected via changes in the behaviour of the fluorescent label (Livak, Genet. Anal. 14, 143 (1999); Tyagi et al., Nat. Biotechnol. 16, 49 (1998)). Hybridization events may occur in liquid phase or with either the probe or the target bound to a solid surface.

An array (microchip) typically consists of thousands of distinct nucleotide probes which are built up in an array on a silicon chip. Nucleic acid to be analyzed is fluorescently labelled, and hybridized to the probes on the chip. This method is one of parallel processing of thousands of probes at once and can tremendously accelerate the analysis. In several publications the use of this method is described (Hacia et al., Nature Genetics 14, 441 (1996); Shoemaker et al., Nature Genetics 14, 450 (1996); Chee et al., Science 274, 610 (1996); DeRisi et al., Nature Genetics 14, 457 (1996), Fan et al., Genome Res, 10, 853 (2000)).

Allele-specific oligonucleotide ligation assays have a high specificity. Oligonucleotides differing in the allele-specific base at the 5′- or 3′-end are only processed in a ligation reaction if they are perfectly bound to the template at the respective oligonucleotide end. This method has been coupled with fluorescence resonance energy transfer (FRET) labeling to create a homogeneous assay system (Chen et al. Genome Res. 8, 549 (1998)). Allele-specific cleavage of a flap probe use the property of recently discovered flap endonucleases (cleavases) to cleave structures created by two overlapping oligonucleotides. In this approach two overlapping oligonucleotides are bound to the polymorphic site. That oligo which has had a perfect match to the target sequence is then detected by the cleavage reaction (Lyamichev et al., Nat. Biotechnol. 17:292 (1999)). Other methods which detect specific base variations usually allow only a lower throughput, such as the allele-specific oligonucleotide (ASO) hybridization. For allele-specific PCR, primers are used which hybridize at their 3′ ends to the target sequence. Only for alleles which are present, a respective PCR product is generated (Ruano and Kidd, Nucleic Acids Res 17, 8392 (1989)). A specificity increasing modification of allele-specific PCR is the Amplification Refractory Mutation System, as disclosed in European Patent Application Publication No. 0332435 and in Newton et al., Nucleic Acids Res 17, 2503 (1989). If the variations lead to changes in the specific recognition sites of nucleic acid processing, enzymes methodologies such as restriction fragment length polymorphism (RFLP) probes or PCR-RFLP methods may also be used to detect these variations.

Detection of SNPs may be accomplished by amplification, for instance by PCR, from genomic or cDNA and sequencing of the amplified nucleic acid or by molecular cloning of the relevant allele and sequencing the allele using techniques well known in the art.

Kit

The present invention also provides kit for diagnosing an autism spectrum disorder (ASD), or predisposition to develop an ASD, in a subject, which comprises a plurality of primer pairs or probes capable of investigating a set of single nucleotide polymorphisms (SNPs) in a sample from the subject, wherein the set of SNPs is as defined above.

The kit may comprise a plurality of probes, each capable of hybridising specifically to one of the alternative forms of the SNP.

As used herein, the term “probe” refers to a nucleic acid (eg. an oligonucleotide or a polynucleotide sequence) that is complementary to a nucleic acid sequence present in a sample, such that the probe will specifically hybridize to the nucleic acid sequence present in the sample under appropriate conditions.

The kit may also comprise means for detecting the presence of a plurality of hybridization products, corresponding to each probe/SNP combination.

The probes may be gene probes, for example oligomeric DNA sequences of 15 to 50 bases which are synthesized with a variant base, to detect the presence of a SNP, or no variant bases, to detect the absence of a SNP.

The probe is then hybridized to the genome under stringent conditions allowing single base variant discrimination.

Alternatively the kit may comprise a plurality of primer pairs, using which each SNP may detected by:

a) amplifying the potential SNP-containing parts of the nucleic acid in said sample,
b) sequencing, e.g. mini-sequencing, the amplified nucleic acids; and
c) detecting the presence or absence of the SNPs in said sample.

The term “primer” as used herein refers to an oligonucleotide which is capable of acting as a point of initiation of synthesis when placed under conditions in which synthesis of a primer extension product which is complementary to a nucleic acid strand is induced, i.e. in the presence of nucleotides and an inducing agent—such as DNA polymerase and at a suitable temperature and pH.

The primers and/or probes may be labelled in order to facilitate their detection. Such labels (also known as reporters) include, but are not limited to, radioactive isotopes, fluorophores, chemiluminescent moieties, enzymes, enzyme substrates, enzyme cofactors, enzyme inhibitors, dyes, metal ions, metal sols, other suitable detectable markers—such as biotin or haptens and the like. Particular example of labels which may be used include, but are not limited to, fluorescein, 5(6)-carboxyfluorescein, Cyanine 3 (Cy3), Cyanine 5 (Cy5), rhodamine, dansyl, umbelliferone, Texas red, luminal, NADPH and horseradish peroxidase.

The probes and/or primers used in the kit hybridise specifically to their target nucleic acid sequence. They may, for example, hybridise under high-stringency conditions.

Stringency of hybridisation refers to conditions under which polynucleic acids hybrids are stable. Such conditions are evident to those of ordinary skill in the field. As known to those of skill in the art, the stability of hybrids is reflected in the melting temperature (Tm) of the hybrid which decreases approximately 1 to 1.5° C. with every 1% decrease in sequence homology. In general, the stability of a hybrid is a function of sodium ion concentration and temperature.

As used herein, high stringency refers to conditions that permit hybridisation of only those nucleic acid sequences that form stable hybrids in 1M Na+ at 65-68° C. High stringency conditions can be provided, for example, by hybridisation in an aqueous solution containing 6×SSC, 5×Denhardt's, 1% SDS (sodium dodecyl sulphate), 0.1 Na+ pyrophosphate and 0.1 mg/ml denatured salmon sperm DNA as non specific competitor.

It is understood that these conditions may be adapted and duplicated using a variety of buffers, e.g. formamide-based buffers, and temperatures. Denhardt's solution and SSC are well known to those of skill in the art as are other suitable hybridisation buffers (see, e.g. Sambrook, et al., eds. (1989) Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York or Ausubel, et al., eds. (1990) Current Protocols in Molecular Biology, John Wiley & Sons, Inc.). Optimal hybridisation conditions have to be determined empirically, as the length and the GC content of the hybridising pair also play a role.

Sample

The sample may be or may be derived from a biological sample, such as a blood sample, cheek swab, a biopsy specimen, a tissue extract, an organ culture or any other tissue or cell preparation from a subject.

In theory, the presence of SNP can be determined by extracting DNA from any tissue of the body.

The sample may be or may be derived from an ex vivo sample.

The sample may be or may be derived from whole blood or a fraction of whole blood.

Suitably, the sample is nucleic acid, such as genomic DNA.

Subject

The subject may be a human. The subject may be a child under 10 years of age. The subject may be a child whose age or mental age is too low for reliable ASD assessment using behavioural tests. For example, the subject may be a child under 18 months of age. Additionally the subject may be an adolescent or adult.

The subject may be pre-implantation or post-implantation foetus.

Foetal cells for analysis can be obtained by amniocentesis, chorionic villus sampling (CVS), or drawing blood from the foetal umbilical cord, using methods known in the art. Pre-natal testing allows the likelihood of a subject to develop an ASD to be determined before birth, so this information can be taken into consideration throughout the child's babyhood and infancy.

Pre-implantation screening may be carried out, for example, during IVF procedures. Genetic material for analysis may be obtained, for example, from polar bodies using known techniques.

The subject may show some symptoms of an ASD. The subject may have been previously characterised as having an ASD by behavioural tests. Where the results of behavioural tests are ambiguous or inconclusive, the method of the present invention may be used to confirm the diagnosis.

The subject may have a family history of ASD.

Solid Support

In the kit of the present invention, nucleic acid probes may be associated with a support or substrate to provide an array of nucleic acid probes to be used in an array assay. Suitably, the probe is pre-synthesized or obtained commercially, and then attached to the substrate or synthesized on the substrate, i.e., synthesized in situ on the substrate.

A specific method of nucleic acid hybridization that can be utilized is nucleic acid chip/array hybridization in which nucleic acids are present on a immobilized surface—such as a microarray and are subjected to hybridization techniques sensitive enough to detect minor changes in sequences.

As used herein, an “array” includes any two-dimensional or substantially two-dimensional (as well as a three-dimensional) arrangement of addressable regions bearing a particular chemical moiety or moieties (e.g., biopolymers—such as polynucleotide or oligonucleotide sequences (nucleic acids), polypeptides (e.g., proteins), carbohydrates, lipids, etc.). The array may be an array of polymeric binding agents—such as polypeptides, proteins, nucleic acids, polysaccharides or synthetic mimetics. Typically, the array is an array of nucleic acids, including oligonucleotides, polynucleotides, cDNAs, mRNAs, synthetic mimetics thereof, and the like. Where the arrays are arrays of nucleic acids, the nucleic acids may be covalently attached to the arrays at any point along the nucleic acid chain, but are generally attached at one of their termini (e.g. the 3′ or 5′ terminus). Sometimes, the arrays are arrays of polypeptides, e.g., proteins or fragments thereof.

Array technology and the various techniques and applications associated with it is described generally in numerous textbooks and documents. These include Lemieux et al., 1998, Molecular Breeding 4, 277-289, Schena and Davis. Parallel Analysis with Biological Chips. in PCR Methods Manual (eds. M. Innis, D. Gelfand, J. Sninsky), Schena and Davis, 1999, Genes, Genomes and Chips. In DNA Microarrays: A Practical Approach (ed. M. Schena), Oxford University Press, Oxford, UK, 1999), The Chipping Forecast (Nature Genetics special issue; January 1999 Supplement), Mark Schena (Ed.), Microarray Biochip Technology, (Eaton Publishing Company), Cortes, 2000, The Scientist 14[17]:25, Gwynne and Page, Microarray analysis: the next revolution in molecular biology, Science, 1999 Aug. 6; and Eakins and Chu, 1999, Trends in Biotechnology, 17, 217-218.

Array technology overcomes the disadvantages with traditional methods in molecular biology, which generally work on a “one gene in one experiment” basis, resulting in low throughput and the inability to appreciate the “whole picture” of gene function. Array technology may be used in the context of the present invention to identify the presence or absence of some or all of the SNPs from the SNp set in the sample from the subject.

The SNP detection system (e.g. probes) may be fixed or immobilised onto a solid phase, preferably a solid substrate, to limit diffusion and admixing of the samples. Probes may be immobilised to a substantially planar solid phase, including membranes and non-porous substrates such as plastic and glass. Furthermore, the probes may be arranged in such a way that indexing (i.e., reference or access to a particular SNP) is facilitated. Typically the probes are applied as spots in a grid formation. Common assay systems may be adapted for this purpose. For example, an array may be immobilised on the surface of a microplate, either with multiple probes in a well, or with a single probe in each well. Furthermore, the solid substrate may be a membrane, such as a nitrocellulose or nylon membrane (for example, membranes used in blotting experiments). Alternative substrates include glass, or silica based substrates. Thus, the probes are immobilised by any suitable method known in the art, for example, by charge interactions, or by chemical coupling to the walls or bottom of the wells, or the surface of the membrane. Other means of arranging and fixing may be used, for example, pipetting, drop-touch, piezoelectric means, ink-jet and bubblejet technology, electrostatic application, etc. In the case of silicon-based chips, photolithography may be utilised to arrange and fix the probes on the chip.

The samples may be arranged by being “spotted” onto the solid substrate; this may be done by hand or by making use of robotics to deposit the sample. In general, arrays may be described as macroarrays or microarrays, the difference being the size of the sample spots. Macroarrays typically contain sample spot sizes of about 300 microns or larger and may be easily imaged by existing gel and blot scanners. The sample spot sizes in microarrays are typically less than 200 microns in diameter and these arrays usually contain thousands of spots. Thus, microarrays may require specialized robotics and imaging equipment, which may need to be custom made. Instrumentation is described generally in a review by Cortese, 2000, The Scientist 14[11]:26. The number of distinct nucleic acid sequences, and hence spots or similar structures (i.e., array features), present on the array may vary, but is generally at least 2, usually at least 5 and more usually at least 10, where the number of different spots on the array may be as a high as 50, 100, 500, 1000, 10,000 or higher, depending on the intended use of the array. The spots of distinct nucleic acids present on the array surface are generally present as a pattern, where the pattern may be in the form of organized rows and columns of spots, e.g., a grid of spots, across the substrate surface, a series of curvilinear rows across the substrate surface, e.g., a series of concentric circles or semi-circles of spots, and the like. The density of spots present on the array surface may vary, but will generally be at least about 10 and usually at least about 100 spots/cm², where the density may be as high as 10⁶ or higher, but will generally not exceed about 10⁵ spots/cm².

Techniques for producing immobilised libraries of DNA molecules have been described in the art. Generally, most prior art methods described how to synthesise single-stranded nucleic acid molecule libraries, using for example masking techniques to build up various permutations of sequences at the various discrete positions on the solid substrate. U.S. Pat. No. 5,837,832, describes an improved method for producing DNA arrays immobilised to silicon substrates based on very large scale integration technology. In particular, U.S. Pat. No. 5,837,832 describes a strategy called “tiling” to synthesize specific sets of probes at spatially-defined locations on a substrate which may be used to produced the immobilised DNA libraries of the present invention. U.S. Pat. No. 5,837,832 also provides references for earlier techniques that may also be used.

The array will include a plurality of different probes of different sequence covalently or non-covalently attached to, different and known locations on the substrate surface. The array may comprise a probe for each SNP in the SNP set.

To aid detection, targets and probes may be labelled with any readily detectable reporter, for example, a fluorescent, bioluminescent, phosphorescent, radioactive, etc reporter. Such reporters, their detection, coupling to targets/probes, etc are discussed elsewhere in this document. Labelling of probes and targets is also disclosed in Shalon et al., 1996, Genome Res 6(7):639-45

Specific examples of DNA arrays are as follow:

Format I: probe cDNA (5005,000 bases long) is immobilized to a solid surface such as glass using robot spotting and exposed to a set of targets either separately or in a mixture. This method is widely considered as having been developed at Stanford University (Ekins and Chu, 1999, Trends in Biotechnology, 1999, 17, 217-218).

Format II: an array of oligonucleotide (20-25-mer oligos) or peptide nucleic acid (PNA) probes is synthesized either in situ (on-chip) or by conventional synthesis followed by on-chip immobilization. The array is exposed to labeled sample DNA, hybridized, and the identity/abundance of complementary sequences are determined. Such a DNA chip is sold by Affymetrix, Inc., under the GeneChip® trademark.

Data analysis is also an important part of an experiment involving arrays. The raw data from a microarray experiment typically are images, which need to be transformed into gene expression matrices—tables where rows represent for example genes, columns represent for example various samples such as tissues or experimental conditions, and numbers in each cell for example characterize the expression level of the particular gene in the particular sample. These matrices have to be analyzed further, if any knowledge about the underlying biological processes is to be extracted. Methods of data analysis (including supervised and unsupervised data analysis as well as bioinformatics approaches) are disclosed in Brazma and Vilo J (2000) FEBS Lett 480(1):17-24.

SNPs may be detected using the BeadXpress Reader System (Illumina Inc., North America). See for example, U.S. Pat. No. 6,355,431. This system is a high-throughput, dual-colour laser detection system that enables scanning of a broad range of multiplexed assays developed using the VeraCode digital microbead technology. Unique VeraCode microbeads are scanned for their code and fluorescent signals, generating highly robust data quickly and efficiently. Downstream analysis is conducted using Illumina's BeadStudio data analysis software or other third-party analysis programs.

The invention will now be further described by way of Examples, which are meant to serve to assist one of ordinary skill in the art in carrying out the invention and are not intended in any way to limit the scope of the invention.

EXAMPLES

Example 1

SVM Analysis of SNPs in Sample Comprising ASD Affected and Unaffected Individuals

Analysis of all individuals in the AGRE sample (see Materials and Methods), ‘Affected’ and ‘Unaffected’, using the ‘leave one out’ method, resulted in an overall accuracy of 87.6%, i.e. the algorithm predicted the correct diagnostic class for 87.6% of the total sample. The percentage accuracy was higher in affected individuals than in unaffected individuals (Table 1).

TABLE 1
Overall classification of all AGRE samples.

	Total Sample (n)	Predicted (n)	Accuracy (%)

ASD Affected	1385	1264	91.3
Unaffected	1494	1253	83.9
Total	2879	2517	87.6
Total sample: the total number of individuals in each diagnostic class.
Predicted: the total number correctly predicted by the SVM algorithm.
Accuracy: the total percentage accuracy achieved.

Using the following formulas, both specificity and sensitivity were measured:

Specificity: True Negatives/True Negatives+False Positives=84%

Sensitivity: True Positives/True Positives+True Negatives=92%

Of the 121 ASD individuals who were misclassified, 42 were twins (41 MZ & 1 DZ) and an additional 4 individuals were quadruplets. 23 of the individuals had a diagnosis of broader autism. 2 of the individuals misclassified had an original diagnosis of autism, however AGRE did not confirm these diagnoses upon their own assessments.

This supports the key assertions behind the invention that (i) clinicians make mistakes using standard diagnostic procedures; and (ii) genetic testing may be used to detect and correct these errors.

Example 2

Analysis with a Reduced Number of “Influential” SNPs

The whole genome sample described in Example 1 used a total of 390671 SNPs to achieve an overall accuracy of 87.6%. Subsequent analysis involved identifying, from the initial analysis, which were most influential to the classification, and repeating the analysis with a reduced number of “influential” (i.e. more highly weighted) SNPs.

The results are shown in FIG. 1 and Table 2.

	TABLE 2
	No of SNPs	Accuracy %

	390671	87.43
	78134	86.73
	39067	75.82
	19534	58.18
	7813	52.07
	3907	89.09
	3126	96.67
	2345	89.00
	1564	73.00

As shown in FIG. 1 and Table 2, maximal accuracy of over 96.6% is achieved with a SNP set of about 3126 of the most highly weighted SNPs from the whole genome sample. These SNPs, together with their respective weights, are shown in Table 3.

Increasing or decreasing the number of SNPs lowers the accuracy of the test, but SNP sets containing between 2345 and 3907 SNPs still result in an accuracy of at least 89%.

Materials and Methods

Sample

The Autism Genetic Resource Exchange (AGRE) sample was used, comprising of 1385 individuals with ASD and 1494 unaffected individuals. A total of 720 families were analysed, with at least one child diagnosed with autism using the ADI-R. The second (and subsequent) affected child had an AGRE classification of autism, broad spectrum (including Asperger's Syndrome and PDD-NOS) or Not Quite Autism (NQA, individuals who are no more than one point away from meeting autism criteria on any or all of the diagnostic domains). Ethnicity and race was self-reported at 69% white, 12% Hispanic/Latino, 10% Unknown, 5% mixed, 2.5% each Asian and African American, less than 1% Native Hawaiian/Pacific Islander and American Indian/Native Alaskan.

Within the total sample, one set of quadruplets', all diagnosed with autism were evident. Six sets of triplets (14 Autism/1 NQA/3 Unaffected), 43 dizygotic and 32 monozygotic twin pairs were noted (1 additional twin pair set, of unknown zygosity was also sampled). Of the individual twins, diagnosis was as follows—123 autism, 12 broad spectrum, 6 NQA, 4 unknown and 7 unaffected. Overall the diagnostic break down of all ASD individuals comprised of 87% autism, 8% broad spectrum and 5% NQA.

Genotyping was conducted using Affymetrix 5.0 chips at the Genetic Analysis Platform of the Broad Institute; full methods are described in (Weiss, L. A., Y. Shen, et al. (2008). N Engl J Med 358(7): 667-75).

Data was downloaded from www.agre.org, comprising of three files

• • 1) .bed—file comprising all generated genotype data
  • 2) .bim—map of all genotyped markers
  • 3) .fam—family structures of all individuals genotyped
    From the .bed file, all SNPs found on the X and Y chromosomes were removed. Due to the high ratio of males to females diagnosed with ASD, it was though this in itself may be influential in the classification. The .bed file was divided into 2879 files, containing genotypic data for one individual.

Support Vector Machine

A SVM analysis, using a linear kernel, was applied to the data using a leave-one-out procedure. In this procedure a single individual is withheld from the SVM training and then tested to assess whether they are affected or unaffected. The leave-one-out procedure was subsequently repeated 2879 times (for each individual) and the results averaged.

	TABLE 3
	SNP	Weight

	rs626479	−0.0172
	rs6680471	−0.0179
	rs6665853	0.0136
	rs4654500	−0.0002
	rs4654511	0.0146
	rs12078298	0.0029
	rs2411738	−0.0024
	rs12049256	0.0126
	rs9439603	0.0332
	rs2312464	0.007
	rs17030082	−0.0119
	rs6679134	0.0119
	rs2506902	0.0056
	rs1292657	0.0323
	rs2487647	0.0312
	rs2744692	0.0198
	rs6694657	−0.0023
	rs10927602	0.0225
	rs6693417	0.0224
	rs17471689	0.0345
	rs1934057	0.0119
	rs732725	−0.0274
	rs2744749	0.0178
	rs11249045	−0.0059
	rs10794668	0.0123
	rs430022	0.0113
	rs311480	−0.0118
	rs566421	−0.0161
	rs4908393	0.0105
	rs4654352	0.0103
	rs7547083	−0.022
	rs6426343	−0.0066
	rs10753264	0.0107
	rs669216	0.0206
	rs2796209	−0.0063
	rs9659735	−0.0331
	rs6696621	−0.0031
	rs2180133	−0.0341
	rs7517274	−0.0307
	rs16825353	−0.0078
	rs10493109	0.0106
	rs1034268	0.0056
	rs7518522	−0.0288
	rs7365614	0.0267
	rs264025	0.0081
	rs945179	0.0239
	rs3766212	0.0178
	rs1502908	−0.0032
	rs1967757	−0.0007
	rs835342	−0.0172
	rs1288332	0.0108
	rs1288587	−0.0191
	rs953625	0.0054
	rs6683597	−0.0282
	rs11206407	0.0034
	rs689258	−0.014
	rs570218	0.0178
	rs498831	0.0165
	rs1749859	−0.0087
	rs3815226	−0.0023
	rs590621	0.0023
	rs13374752	0.0281
	rs3992634	0.0197
	rs1331855	0.0264
	rs706370	−0.0104
	rs1557061	0.0153
	rs3005896	0.0012
	rs12138574	0.0033
	rs4477326	0.0222
	rs4912230	0.0236
	rs1524707	0.0257
	rs4244011	−0.0018
	rs2760484	0.0325
	rs6679571	0.0214
	rs585368	−0.02
	rs11207575	0.0167
	SNP_A-	−0.0021
	4277480
	rs11207831	−0.0086
	rs10889383	−0.0088
	rs6662848	−0.004
	rs2179811	−0.0227
	rs6588064	0.0146
	rs825191	−0.0152
	rs2186122	−0.0038
	rs7551528	0.0004
	rs7516251	0.0142
	rs2815351	−0.0063
	rs17375018	0.0327
	rs12083789	−0.032
	rs288929	0.0061
	rs617344	0.0092
	rs11209455	0.0106
	rs10736412	0.0198
	rs11162095	0.0343
	rs9970671	−0.0282
	rs4949764	−0.0111
	rs10493649	−0.0022
	rs17105569	0.0247
	rs10874241	−0.0312
	rs7542573	−0.0193
	rs12057556	−0.0203
	rs772604	0.0247
	rs10782601	−0.0071
	rs6662386	−0.0021
	rs1188909	−0.0228
	rs4414050	0.0123
	rs2249591	−0.031
	rs12565150	−0.0068
	rs12132107	0.0028
	rs2064662	0.0043
	rs6693882	0.0195
	rs1889060	−0.0212
	rs12757095	−0.0299
	rs387176	0.0194
	rs396954	0.0198
	rs12137571	−0.0226
	rs2336015	0.0066
	rs11577194	0.0076
	rs17631306	0.0158
	rs3393	0.0183
	SNP_A-	0.0231
	1962128
	rs11102368	−0.0063
	rs4839202	−0.0054
	rs11102374	−0.0197
	rs2998359	−0.0262
	rs4838994	0.0277
	SNP_A-	0.0168
	4240881
	rs12145661	−0.009
	rs568359	0.0102
	rs4659126	0.0069
	rs1325939	0.0231
	rs2275236	−0.0252
	rs10494273	0.0233
	rs6587671	−0.0089
	rs1923496	−0.0158
	rs2146116	−0.0039
	rs10908448	−0.0233
	rs954916	−0.0101
	rs9427242	0.0002
	rs822430	0.0088
	rs4661138	0.0121
	rs11265186	−0.0272
	rs2501348	0.0104
	rs4596880	−0.0061
	rs2490438	0.0084
	rs10919117	−0.0135
	rs2661810	−0.0058
	rs11580624	−0.0167
	rs10753642	0.0012
	rs1494409	0.0172
	rs1494408	−0.0016
	rs6675438	−0.0205
	rs1343546	0.0138
	rs2027573	0.019
	rs6697712	−0.0012
	rs6426958	0.0071
	rs1532482	0.0145
	rs869714	0.0057
	rs885458	−0.011
	rs2143312	0.0074
	rs10918931	−0.007
	rs16863926	−0.0196
	rs2224396	0.0179
	rs12075807	0.0025
	rs484686	−0.0337
	rs912300	0.0264
	rs6694387	0.0145
	rs9425287	−0.0049
	rs6689901	0.0121
	rs10732999	0.0205
	rs10913355	0.0273
	rs12033565	−0.0189
	rs7522303	−0.001
	rs2811280	−0.0054
	rs1570807	0.0144
	rs3845427	0.0159
	rs477956	−0.0165
	rs6703122	0.0068
	rs6692352	−0.005
	rs6704003	−0.012
	rs10911583	0.0191
	rs6424983	−0.0263
	rs10801058	0.0304
	rs1568133	0.0114
	rs16836373	−0.0067
	rs634727	−0.018
	rs10733086	−0.0013
	rs1332660	0.0176
	rs2224873	−0.0369
	rs2359372	0.0093
	rs3767735	0.0352
	rs1892432	0.003
	rs2249156	−0.0006
	rs2248967	−0.0006
	rs1572789	−0.0126
	rs6700264	0.0191
	rs2486933	0.0146
	rs2486942	−0.0074
	rs4543864	0.0173
	rs1997034	0.0178
	rs2282450	−0.0065
	rs12407361	−0.0078
	rs11579772	−0.0008
	rs7552993	0.0033
	rs684431	0.0107
	rs1933573	−0.0169
	rs3753522	0.0057
	rs1962735	0.0239
	rs2294850	−0.0073
	rs926579	0.0037
	rs10863900	−0.0007
	rs4951534	0.0229
	rs1509866	0.0154
	rs11117658	0.0307
	rs11117796	−0.0148
	rs1602269	−0.0023
	rs2798631	0.0174
	rs10863404	−0.0094
	rs4428898	−0.023
	rs6694088	0.0098
	rs2210977	−0.0024
	rs2970199	−0.0255
	rs2790762	0.0019
	rs6679430	0.0233
	rs6676201	−0.0041
	rs710824	−0.0151
	rs10916464	−0.0109
	rs7538437	−0.014
	rs1160075	−0.0268
	rs867844	−0.0214
	rs6698696	0.0051
	rs549209	−0.0044
	rs6659304	0.0055
	rs4659838	−0.022
	rs291356	−0.0072
	rs2853599	−0.01
	rs2385494	−0.0077
	rs1266164	0.006
	rs1252252	0.0001
	rs4659743	0.0021
	rs10802581	−0.0097
	rs4659491	−0.009
	rs16835116	0.011
	rs7525233	−0.0271
	rs6657299	−0.0227
	rs1984165	0.007
	rs10926147	−0.0228
	rs1539098	0.0146
	rs3765814	−0.0013
	rs6669036	0.0049
	rs6696527	−0.0018
	rs2809979	−0.0081
	rs10926897	0.0217
	rs544739	−0.0016
	rs2786694	−0.0119
	rs1093961	0.0034
	rs1069217	0.0056
	rs10924359	−0.0218
	rs3124124	0.0171
	rs6671004	0.0129
	rs2386548	−0.0136
	rs1144812	0.0244
	rs4382761	−0.0093
	rs1364648	0.0155
	rs1451196	0.0086
	rs6738683	0.0168
	rs6548128	−0.0314
	rs10174005	0.0115
	rs792105	−0.0052
	rs7604344	−0.0249
	rs1381514	0.0102
	rs1461312	−0.0205
	rs13382991	−0.001
	rs10167072	−0.0093
	rs17681545	0.0194
	rs7572345	0.0197
	rs328634	−0.0259
	rs1003187	−0.0094
	rs4668666	−0.0266
	rs6432085	−0.0011
	rs16856887	0.0039
	rs7594176	−0.0297
	rs10167277	−0.0033
	rs1225214	0.0352
	rs6432270	0.0071
	rs17344070	−0.0206
	rs2571642	−0.0347
	rs10189450	0.0221
	rs10803676	0.0235
	rs13385499	−0.0305
	rs12714264	0.0071
	rs312970	0.0016
	rs576203	0.0065
	rs7570872	−0.0207
	rs11127215	−0.0142
	rs207413	−0.0111
	rs212708	0.0102
	rs1534350	0.0018
	rs4670550	0.0013
	rs1015696	−0.0171
	rs7588747	0.0251
	rs173023	−0.0186
	rs13010748	0.0096
	rs297150	−0.0056
	rs7582507	−0.0215
	rs6746891	0.0119
	rs11688286	0.0207
	rs1517024	0.0361
	rs4082957	−0.0028
	rs17033378	0.0165
	rs11678872	0.016
	rs12712996	0.0064
	rs1574380	0.0048
	rs6738387	−0.0048
	rs2300443	−0.0124
	rs13000157	−0.0153
	rs7355586	−0.0374
	rs17489439	−0.0243
	rs1159982	0.006
	rs12328023	0.0287
	rs7557799	0.0276
	rs2033411	−0.0211
	rs4672103	−0.0027
	rs11673760	−0.0195
	rs820985	0.0133
	rs759250	0.0345
	rs11691214	−0.0023
	rs4671398	−0.0061
	rs1011066	−0.0029
	rs17692393	−0.0008
	rs2540959	−0.0126
	rs1420183	−0.0284
	rs11678288	0.0036
	rs719832	−0.0111
	rs6743599	−0.0051
	rs3771527	0.0125
	rs12617676	−0.0058
	rs4852430	−0.0143
	rs4510249	0.0007
	rs1455385	0.0065
	rs6738834	−0.0101
	rs17022288	0.0106
	rs6749875	0.0355
	rs11689667	−0.0103
	rs883650	−0.0062
	rs9677221	−0.0079
	rs6738956	−0.0114
	rs9309628	−0.0345
	rs17704088	−0.0072
	rs6713310	0.0046
	rs921423	0.0167
	rs1010387	0.0322
	rs4632400	0.0056
	rs17493655	−0.0018
	rs11686712	−0.0126
	rs11683001	−0.0431
	rs4603782	0.0026
	rs12712130	0.0053
	rs1861228	−0.0229
	rs12998183	−0.015
	rs6543426	0.0031
	rs1524297	0.0081
	rs1524287	0.0079
	rs4676274	−0.0125
	rs6754115	−0.0191
	rs884448	0.0104
	rs10181720	0.0236
	rs17047362	−0.0024
	rs6737733	0.004
	rs1433526	−0.0105
	rs17584619	−0.0147
	rs10207133	−0.0125
	rs11679589	0.0068
	rs718867	0.017
	rs297479	−0.017
	rs299566	−0.0109
	rs13002629	−0.0133
	rs12468639	−0.0112
	rs11123081	−0.0233
	rs13390374	0.0356
	rs831360	−0.0116
	rs3820757	0.0187
	rs4150477	0.009
	rs840875	−0.0212
	rs7578253	0.0087
	rs7586789	0.0169
	rs6431151	0.0241
	rs6749712	0.0011
	rs6727803	−0.0171
	rs1446749	0.0114
	rs16831992	0.0075
	rs936835	−0.0145
	rs443719	0.0337
	rs10197153	−0.0294
	rs351673	0.0042
	rs9287378	0.0115
	rs13421583	−0.0263
	rs6430136	0.0004
	rs1356738	0.0334
	rs1519768	0.0137
	rs1519800	−0.0112
	rs298247	−0.0078
	rs16842681	−0.0024
	rs12692654	−0.0037
	rs1369252	0.0005
	rs2083482	−0.0345
	rs4340537	0.0048
	rs6732793	−0.0132
	rs1540821	0.0156
	rs700542	0.0235
	rs17251018	0.0007
	rs741378	0.019
	rs7590275	−0.0045
	rs10206361	0.0189
	rs2194720	0.0091
	rs3754764	0.0147
	rs2113807	0.0271
	rs4893825	0.0445
	rs4893966	0.0232
	rs6751992	−0.0034
	rs260059	0.0244
	rs10186570	−0.0158
	rs12466031	−0.0348
	rs2368384	−0.0149
	rs11902682	−0.0012
	rs826168	−0.0311
	rs7560722	0.0057
	rs10166420	0.0297
	rs887701	−0.0161
	rs1378156	−0.0215
	rs4853575	−0.0173
	rs7558504	−0.0068
	rs10931635	−0.0123
	rs1392658	0.0158
	rs11888904	−0.0154
	rs7558972	−0.0314
	rs12614240	0.0117
	rs295134	0.0197
	rs4233996	0.0039
	rs295149	0.0309
	rs1369841	0.0181
	rs11892551	−0.0171
	rs6757529	0.0117
	rs10195536	−0.0232
	rs3820901	0.0144
	rs16844213	0.0098
	rs3821136	−0.022
	rs10167685	0.015
	rs2662683	−0.0284
	rs4673821	0.0126
	rs12612329	−0.0136
	rs828910	0.0079
	rs17779951	0.0155
	rs750764	−0.0123
	rs17226763	−0.0064
	rs6723377	0.0106
	rs6733393	−0.0056
	rs1371551	0.0095
	rs3845841	0.0072
	rs1596395	0.0107
	rs9288601	−0.0184
	rs10208046	0.0171
	rs6744449	0.0087
	rs3811514	−0.0451
	rs6436790	0.0171
	rs3106301	0.0177
	rs6437020	0.0145
	rs13395911	−0.0165
	rs689101	−0.0064
	rs972513	0.0132
	rs2316434	0.0076
	rs7571980	−0.0146
	rs2292708	0.0099
	rs10192532	−0.0154
	rs2573712	0.0297
	rs10187736	−0.0219
	rs821501	−0.0279
	rs7608438	−0.0093
	rs2648466	0.0306
	rs1827106	−0.0078
	rs11128826	−0.0376
	rs4434131	−0.0194
	rs1143977	−0.0028
	rs1032783	0.002
	rs1666325	−0.0204
	rs17018141	0.004
	rs427502	−0.0226
	rs17023520	−0.0172
	rs4685803	0.0167
	rs3804992	−0.0004
	rs7637793	0.0029
	rs11130339	−0.023
	rs286588	0.0053
	rs10490889	0.0086
	rs2117788	0.0025
	rs5010733	−0.0233
	rs571701	−0.0125
	rs341795	−0.0003
	rs420537	−0.0238
	rs709165	−0.0188
	rs299651	−0.0022
	rs1618545	0.0136
	rs2633442	0.0035
	rs9831765	0.0225
	rs2881980	0.0149
	rs4684901	0.0128
	rs4684131	0.0147
	rs9865654	0.0164
	rs981694	0.0074
	rs1587378	0.0393
	rs10510530	0.023
	rs13317243	0.0072
	rs6550755	−0.0193
	rs9863976	−0.0219
	rs13072262	0.0182
	rs322680	−0.0127
	rs922289	0.0186
	rs17026472	−0.0023
	rs12630254	−0.013
	rs6781673	0.0338
	rs9853831	−0.0089
	rs25506	0.0244
	rs2197728	0.0074
	rs6799641	0.0074
	rs999745	−0.0334
	rs6806372	0.0178
	rs2693477	0.0072
	rs7431934	0.0067
	rs416183	−0.013
	rs7639483	0.0287
	rs9846284	0.0018
	rs2191031	−0.0272
	rs13314659	0.0141
	rs4446245	0.0116
	rs17054250	−0.0013
	SNP_A-	−0.0064
	1880070
	rs3864001	−0.0033
	rs4083342	−0.0065
	rs17054340	−0.0062
	rs1461796	0.0288
	rs6445725	0.0154
	rs9683127	0.0113
	rs641035	0.0195
	rs4637258	−0.0104
	rs11708862	0.005
	rs734184	0.0158
	rs2366968	0.033
	rs17258256	−0.0314
	rs6785140	−0.0136
	rs9834435	−0.0221
	rs9840800	0.0292
	rs11918950	−0.0173
	rs2600859	−0.0137
	rs17066787	0.0193
	rs4254651	−0.0189
	rs6445454	0.0033
	rs11705970	0.0079
	rs7627319	−0.0332
	rs6772684	0.0151
	rs1288824	0.0041
	rs13087868	−0.0045
	rs4677093	0.0065
	rs6549724	−0.0039
	rs17745164	0.0231
	rs9852230	0.0188
	rs6548639	−0.0086
	rs17749340	−0.0005
	rs13327580	0.0184
	rs6795503	0.0003
	rs17018312	−0.0336
	rs2372744	0.0014
	rs2639220	0.0101
	rs6548788	0.0261
	rs9859573	−0.0115
	rs291961	0.0091
	rs188349	0.0062
	rs10511061	0.0284
	rs11127978	−0.0104
	rs6767207	−0.0136
	rs1355226	−0.0038
	rs724972	−0.0152
	rs7631619	−0.0077
	rs6785834	−0.0008
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	rs16830554	0.0138
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	rs6784753	0.018
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	rs1553191	−0.0184
	rs9289082	−0.0078
	rs3772132	0.0003
	rs1259494	0.0008
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	rs10804716	−0.0104
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	rs2700856	0.0138
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	rs7649045	−0.0229
	rs6600769	0.0174
	rs1014947	−0.0121
	rs1419046	0.0007
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	rs2471347	0.0134
	rs2968684	−0.0403
	rs7693293	−0.0044
	rs6838792	0.002
	rs750518	0.0006
	rs3821920	−0.0204
	rs12505562	0.0393
	rs16837510	0.0124
	rs16837871	0.02
	rs6826497	−0.0068
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	rs6824720	−0.0333
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	rs755403	0.0279
	rs4569755	−0.0231
	rs1344223	−0.0083
	rs1048506	−0.0289
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	rs6448968	−0.0146
	rs10489083	−0.0091
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	rs4698515	0
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	rs12507442	0.0262
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	rs12646671	0.0094
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	rs877323	−0.0119
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	rs169778	−0.0089
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	rs10517186	0.0019
	rs11930738	−0.0057
	rs902658	0.0284
	rs874498	0.0261
	rs16990031	0.007
	SNP_A-	−0.0057
	2123334
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	rs7690708	−0.0037
	rs6829898	0.0137
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	rs7691499	−0.0088
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	rs895087	−0.0028
	rs530222	−0.0032
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	rs7694527	0.0066
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	SNP_A-	0.0202
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	SNP_A-	−0.0141
	1980768
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	rs17652917	0.0039
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	rs6880477	0.0019
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	SNP_A-	−0.0366
	2244745
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	rs12521065	−0.0029
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	rs17461842	−0.0075
	rs6898671	−0.0154
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	SNP_A-	−0.0158
	1831010
	rs3911203	−0.001
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	SNP_A-	0.0194
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	rs7765472	−0.0039
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	rs2275446	0.0006
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	rs4331986	0.0063
	rs4278019	0.0178
	rs10948783	0.0063
	rs10456715	0.0196
	rs3807016	0.0049
	rs4598117	0.0014
	rs12195233	−0.0239
	rs10494880	0.0075
	rs10944779	−0.0096
	rs6924505	−0.01
	rs9363384	−0.0292
	rs354369	0.0122
	rs902287	−0.0118
	rs1938091	−0.0081
	rs12202347	0.0043
	rs10806546	0.0058
	rs10944941	0.0285
	rs1454729	−0.0124
	rs9342711	0.0237
	rs1204331	−0.021
	rs2250128	−0.0086
	rs7762984	−0.023
	rs10805985	0.0146
	rs4235880	0.0201
	rs6454237	−0.0065
	rs1145866	−0.0258
	rs2048645	0.0025
	rs7767180	0.0063
	rs10943976	−0.0022
	rs4527671	−0.0045
	rs7742127	0.0028
	rs4706205	0.0035
	rs9362567	−0.0079
	rs205199	0.0106
	rs2150817	−0.017
	rs16883568	0.0036
	rs7750014	−0.0084
	rs6924365	0.0079
	rs4299799	0.0202
	rs4235842	0.0213
	rs1535334	0.0165
	rs462846	0.0024
	rs4839985	−0.0078
	rs550848	0.0323
	rs2894889	0.0101
	rs12182308	−0.0048
	rs1996911	−0.0036
	rs9391057	0.0277
	rs9377620	0.0095
	rs4945654	0.0237
	rs4946889	−0.0254
	rs13210693	0.0265
	rs1111865	0.0055
	rs7752501	0.0291
	rs10872088	−0.0033
	rs6919507	0.0148
	rs9387334	−0.0074
	rs10484296	0.0218
	rs11153594	−0.0197
	rs9481704	−0.0008
	rs580435	0.0417
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	rs6941346	0.0007
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	rs7738249	0.0139
	rs9490745	−0.0173
	rs17085362	−0.0103
	rs9385301	−0.0216
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	rs2626094	0.0189
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	rs9493185	−0.0188
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	rs211591	−0.0018
	rs6901981	−0.0096
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	rs9494756	−0.0335
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	rs2498644	−0.0202
	rs9403045	0.0197
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	rs2328165	−0.0072
	rs9389952	0.0015
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	rs9390356	−0.0019
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	rs6915298	0.0005
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	rs2169976	−0.005
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	rs9371823	−0.0021
	rs6899710	−0.0284
	rs9384361	−0.0014
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	rs2358	0.0192
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	rs1021544	0.0064
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	rs11772124	0.0045
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	rs1017266	0.0045
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	rs12333630	−0.0011
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	rs6461292	0.0028
	rs1403795	−0.015
	rs2192486	−0.0157
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	SNP_A-	0.0211
	4199965
	rs1524102	−0.0005
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	rs11763233	0.0222
	rs17723231	−0.0136
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	rs1419791	0.0256
	rs12530870	0.0021
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	rs9649729	−0.0007
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	rs601899	−0.0008
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	rs10499728	−0.0014
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	rs11974847	0.0193
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	rs7794971	0
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	rs3801767	−0.0039
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	rs31669	0.0008
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	SNP_A-	−0.0182
	1931004
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	rs2117616	0.0005
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	SNP_A-	0
	1992337
	rs11776929	0.0394
	SNP_A-	0.0012
	1950935
	rs7812436	0.02
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	SNP_A-	−0.0365
	2172528
	rs16879344	−0.0187
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	SNP_A-	0.014
	1877597
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	rs720600	−0.014
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	rs1906470	0.0223
	rs4314953	−0.008
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	rs3907196	0.0011
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	rs11595067	−0.009
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	rs11200819	−0.0006
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	rs1120669	−0.0076
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	rs389094	−0.0308
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	rs10884820	−0.0102
	rs11194926	−0.0226
	rs7085888	−0.007
	rs5015510	−0.015
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	rs3781383	0.0111
	rs10787490	0.0141
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	rs11199602	−0.0035
	rs7911084	−0.0107
	rs4751810	−0.0039
	rs11598706	0.0086
	rs12774403	0.0219
	rs4131643	0.0282
	rs7078763	0.0184
	rs10901792	0.0033
	rs11245346	−0.0044
	rs12768691	0.0087
	rs919831	0.0076
	rs10901402	−0.0048
	rs10794008	0.0027
	rs10794069	0.0381
	rs7094049	0.0049
	rs11245205	0.006
	rs12359365	−0.0115
	rs3740547	−0.0258
	rs10764953	−0.0049
	rs12416649	0.0216
	rs1655129	−0.0018
	rs11017552	−0.0134
	rs7075233	−0.0395
	rs4897747	0.007
	rs2814184	0.0054
	rs6578397	−0.0128
	rs17313216	−0.0073
	rs1009240	0.0135
	rs2445335	0.0215
	rs4758441	−0.011
	rs14229	0.0006
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	rs10831786	−0.0277
	rs2165484	0.0063
	rs4539308	0.0049
	rs4757128	−0.0157
	rs10832000	−0.0223
	rs1358560	−0.0287
	rs12417170	−0.0157
	rs4757001	−0.0233
	rs2249878	0.0015
	rs1353649	−0.0018
	rs10833522	−0.0011
	rs11026573	−0.0215
	rs10450560	−0.0021
	rs10833888	0.0059
	rs7101766	−0.0207
	rs2403995	−0.0179
	rs12222611	0.0192
	rs293940	0.0293
	rs7943538	0.0126
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	rs12363459	0.0104
	rs7951928	0.019
	rs208107	0.0185
	rs1806156	0.0209
	rs10767929	0.0032
	rs2207549	−0.0078
	rs4756695	0.0059
	rs10836202	0.0083
	rs704730	0.007
	rs1584807	0.0248
	rs7952315	0.0221
	rs4756190	0.029
	SNP_A-	0.0476
	4206828
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	rs10836978	0.0073
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	rs2958851	0.0021
	rs7128465	0.0173
	rs995034	−0.0242
	rs6485443	−0.0335
	rs11037928	−0.0188
	rs7926989	0.0186
	rs2863165	0.0132
	rs7951225	−0.0233
	rs1052373	0.0115
	rs11040267	0.0129
	rs11040509	−0.0034
	rs10897215	0.0067
	rs1675131	0.0286
	rs2236647	−0.0053
	rs10895987	0.0002
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	rs10793403	0.0224
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	rs2005145	0.0049
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	rs7924613	−0.0053
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	rs7950813	−0.038
	rs596737	−0.0257
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	rs7121417	−0.0071
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	rs7479457	−0.0288
	rs11019558	0.0326
	rs10741438	0.0117
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	rs10830988	−0.0015
	rs16918677	−0.0116
	rs2045459	0.0121
	rs610611	−0.0255
	rs586026	−0.0035
	SNP_A-	−0.007
	2258976
	rs11021058	−0.0039
	rs1943520	0.0081
	rs7938152	−0.002
	rs10501838	−0.015
	rs1690595	0.0217
	rs347759	−0.0085
	rs7926001	0.0423
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	rs1648136	0.0005
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	rs4938659	−0.0144
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	rs2874773	−0.0245
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	rs10790928	0.0364
	rs7948351	0.0153
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	rs1065	−0.0328
	rs11607783	0.0242
	rs4073610	−0.0219
	rs4638317	0.0139
	rs498602	0.0124
	rs10458997	0.01
	rs880159	0.0091
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	rs1015832	−0.0331
	rs764220	−0.0104
	rs11063875	−0.0167
	rs10466913	−0.0009
	rs7971637	0.0038
	rs4883421	0.0147
	rs4628755	0.0006
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	rs7963875	0.0136
	rs6488328	0.013
	rs2416946	0.0094
	rs1351227	0.0074
	rs2300242	0.0388
	rs1461039	−0.0033
	rs1861595	0.0036
	rs17442423	0.0044
	rs1912879	0.0322
	rs6486975	0.0086
	rs10770712	0.0374
	rs4762694	−0.0133
	rs7313671	0.0038
	rs10841833	−0.0186
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	rs10505944	0.0268
	rs11047761	0.0179
	rs859157	−0.0192
	rs17337853	−0.0145
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	rs6487856	−0.0141
	rs7134917	−0.0118
	rs7972251	−0.0039
	rs7306223	−0.0242
	rs1948034	−0.0225
	rs4356315	0.0033
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	rs7310430	−0.0266
	rs12581243	−0.0083
	rs7974787	0.0052
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	rs11182199	−0.0371
	rs12319024	−0.0049
	rs9783418	−0.0023
	rs4251466	0.014
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	rs12322783	0.0087
	rs12818741	0.0087
	rs1995691	−0.0181
	rs868884	−0.018
	rs10783448	−0.0155
	rs10783455	−0.0149
	rs771998	−0.0068
	rs10876222	−0.0032
	rs10783486	0.0199
	rs10747650	−0.0113
	rs17118865	0.0244
	rs12368653	0.0399
	rs3907516	−0.022
	rs2712498	0.0037
	rs12230817	0.0091
	rs10784180	0.0182
	rs11173906	0.0048
	rs1245652	0.0143
	rs4093902	−0.0139
	rs11174764	0.0187
	rs7977987	−0.0188
	rs903088	−0.0172
	rs7960495	−0.0105
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	rs2700078	−0.0226
	rs10878749	0.0197
	rs7977634	0.0187
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	rs1584726	0.0009
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	rs10748140	−0.0189
	rs10784885	0.0028
	rs11178577	−0.0114
	rs2956436	0.0152
	rs17111905	−0.0109
	rs11180220	−0.0122
	rs11180638	0.0183
	rs11104254	−0.034
	rs10862334	−0.0082
	rs7313736	0.0067
	rs17005594	−0.0152
	rs7297808	−0.0112
	rs7300188	−0.0304
	rs10777020	−0.0192
	rs796041	−0.0266
	rs17458324	−0.0013
	rs2520524	0.0187
	rs4842533	0.034
	rs10777370	−0.0039
	rs337662	−0.0128
	rs420271	0.002
	rs6538421	0.0156
	rs7486464	0.0092
	rs1345015	−0.0187
	rs10860097	0.029
	rs4394881	−0.0127
	rs7136406	−0.0182
	rs7965985	0
	rs10860535	0.0095
	rs1488728	−0.0196
	rs1488729	0.026
	rs7975540	−0.0254
	rs35714	−0.0159
	rs4764773	0.0133
	rs10745901	0.0113
	rs1607688	0.0032
	rs11111537	−0.0169
	rs7295941	0.0198
	rs10778419	0.0323
	rs5009017	−0.012
	rs10778619	−0.0104
	rs2888967	−0.0178
	rs10778624	−0.0188
	rs1044994	−0.035
	rs7952972	−0.0058
	rs7309495	0.0018
	rs857747	0.0175
	rs6490034	0.0141
	rs7953468	0.0072
	rs540520	−0.0164
	rs2292681	0.0311
	rs367997	−0.0042
	rs7970937	−0.0009
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	rs4765122	0.0143
	rs2569817	0.008
	rs7967551	−0.0095
	rs2699061	0.0058
	rs7957166	−0.0172
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	rs182046	−0.0285
	rs4760022	0.0018
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	rs9579838	−0.0254
	rs3088071	0.0113
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	rs540109	0.0019
	rs523656	−0.0149
	rs2858809	−0.0149
	rs797212	−0.0214
	rs7982817	0.017
	rs9315248	0.0263
	rs1578831	−0.0376
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	rs1028671	−0.0172
	rs9315768	−0.0284
	rs7323630	0.0199
	rs9532603	0.0089
	rs1887744	−0.0031
	rs1328469	0.0001
	rs9594602	−0.0102
	rs7994531	0.0015
	rs7334307	0.0003
	rs1044856	0.0025
	rs12870438	0.0085
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	rs4942447	−0.0126
	rs9526114	−0.0144
	rs1998891	−0.001
	rs9534732	−0.0018
	rs7322355	0.0022
	rs1951944	0.0032
	rs7984167	0.0033
	rs2181958	−0.0196
	rs9534843	0.0065
	rs1966197	−0.0001
	rs7317431	−0.0286
	rs9568182	0.0265
	rs11619997	0.0115
	rs2066647	−0.026
	rs1359614	−0.0205
	rs6561620	0.0239
	rs9526823	−0.0191
	rs9536222	−0.025
	rs1360776	−0.0331
	rs2000159	0.0186
	rs1341545	0.0146
	rs2592873	−0.0138
	rs1410269	−0.0151
	rs7986718	−0.0162
	rs9538393	−0.009
	rs965038	−0.013
	rs1412973	0.0034
	rs9570966	0.0144
	rs2875357	−0.0104
	rs12584386	−0.0146
	rs1496154	0.0029
	rs4883781	−0.0222
	rs8000469	0.0367
	rs9541348	−0.0115
	rs1604614	−0.0061
	rs9564577	0.0219
	rs9317873	0.0225
	rs9542388	−0.006
	rs9529777	0.0014
	rs2769502	−0.0105
	rs9599836	0.0057
	rs9529875	0.031
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	rs9543259	−0.0133
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	rs7338981	−0.0344
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	rs1348292	−0.015
	rs9586099	0.0156
	rs1855389	−0.0023
	rs1472716	0.0168
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	SNP_A-	0.0002
	2179996
	rs9524382	0.0028
	rs17699419	−0.0031
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	rs9518494	−0.0252
	rs7338828	−0.0045
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	rs7318970	0.0128
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	rs1926530	0.0019
	rs1926528	0.0016
	rs558322	−0.0218
	rs1411766	0.0112
	rs660433	−0.0164
	rs9515279	−0.0036
	rs12870443	−0.0011
	rs9560075	−0.0346
	rs9577771	0.0259
	rs9550117	−0.0211
	rs3814260	−0.0211
	rs10141075	0.0025
	rs1953558	−0.0322
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	rs12590446	0.0002
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	rs2146631	−0.0248
	rs4423349	−0.0216
	rs7152910	−0.0012
	rs8012040	−0.0148
	rs1956404	0.0378
	rs10483377	−0.0229
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	rs10483369	0.0149
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	rs941746	−0.012
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	rs6571668	0.0373
	rs7342504	0.0159
	rs7142040	0.0196
	rs12434601	0.0236
	rs10483469	−0.0064
	SNP_A-	−0.0134
	4289935
	SNP_A-	−0.0134
	2156181
	rs7160534	−0.0098
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	rs17121913	0.0036
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	rs8012357	−0.0005
	rs12431592	0.0021
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	rs1028897	−0.0039
	rs17101715	0.0229
	rs4902293	−0.016
	rs4902439	−0.0102
	rs2478037	−0.0058
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	rs17182558	−0.029
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	rs6574590	−0.02
	rs2372083	0.01
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	rs762018	−0.0011
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	rs8008996	0.0191
	rs8007791	0.0183
	rs2749508	−0.0177
	rs4900178	0.0193
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	rs881163	−0.0241
	rs12896319	0.006
	rs730828	−0.0166
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	rs1022704	−0.0202
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	rs11621256	0.0061
	rs1951570	−0.0087
	rs6576461	0.018
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	rs12439488	−0.0107
	rs579178	0.0102
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	rs2663089	0.0033
	rs12148764	0.0145
	rs410665	0.0074
	rs4776125	0.0128
	rs2171476	0.008
	rs1906409	−0.0034
	rs10518752	0.0037
	rs7172540	0.0313
	rs2414270	−0.031
	rs4776222	0.0105
	rs11071129	0.0002
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	rs935319	0.0083
	rs1280419	−0.028
	rs11638057	0.0261
	rs536431	−0.0069
	rs629876	−0.0115
	rs1979422	0.02
	rs931971	−0.0134
	rs7165960	0.0061
	rs7163836	−0.0088
	rs9972449	0.0149
	rs3825957	0.0097
	rs4775349	0.0357
	rs8032386	0.0114
	rs990526	−0.012
	rs955529	−0.0325
	rs2292862	0.006
	rs982077	0.0115
	rs12907875	−0.0067
	rs7497831	−0.0029
	rs11634726	0.0233
	rs2114716	0.0023
	rs8029934	0.0166
	rs7169595	0.0023
	rs2343675	−0.0154
	rs16940541	−0.0015
	rs870689	−0.0057
	rs8041327	−0.0354
	rs1908786	−0.0271
	rs16977786	0.0028
	rs11073678	0.0189
	rs4450366	−0.006
	rs11073758	−0.0089
	rs939590	0.0184
	rs11858077	−0.0285
	rs293365	−0.0114
	rs7164703	−0.0013
	rs2074084	−0.0051
	rs12437999	0.0345
	SNP_A-	0.0238
	1858077
	rs12903400	−0.0182
	rs11074097	−0.0194
	rs2130100	0.0125
	rs16948232	−0.0211
	rs7172568	0.0548
	rs1433943	−0.0001
	rs11073398	0.0012
	rs7163584	−0.0188
	rs172350	−0.0087
	rs8036556	−0.0114
	rs1036521	−0.0057
	rs2654534	−0.0145
	rs12910002	0.0171
	rs11631447	0.0147
	rs11630665	0.0211
	rs4334280	−0.0093
	rs3923686	0
	rs11635298	0.0134
	rs8036374	0.0132
	rs11854332	0.0154
	rs3784482	0.036
	rs11649508	−0.0277
	rs3760030	0.0151
	rs1919056	0.0417
	rs8057236	−0.0065
	rs11643962	−0.005
	rs9931343	0.0075
	rs1946127	0.0195
	rs17440477	0.0094
	rs1388807	0.0142
	rs2881438	−0.0265
	rs7205378	−0.0242
	rs11859000	−0.0372
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	rs7201849	0.0075
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	rs7201430	−0.0282
	rs7199390	0.0337
	rs11074996	0.0167
	rs1230896	0.0134
	rs2903305	−0.0031
	rs2903308	0.01
	rs1428402	0.0052
	rs1050162	0.0099
	rs1840186	−0.0144
	rs2764776	0.0009
	rs1014632	0.0061
	rs9922232	0.0151
	rs7499763	0.0104
	rs2521681	0.0173
	rs722516	−0.0092
	rs6563855	0.0107
	rs3944008	−0.0233
	rs9935379	0.0073
	rs7403957	0
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	rs12599310	−0.0465
	rs9940629	0.0225
	rs12149010	0.011
	rs4784376	0.023
	rs9929277	−0.0051
	rs11863682	−0.0103
	rs1809348	0.0045
	rs37358	0.01
	rs37363	0.0144
	rs2731740	0.0143
	rs9926973	0.0091
	rs2407755	−0.0035
	rs7500302	−0.001
	rs30888	−0.0156
	rs1510212	0.0052
	rs2331446	0.003
	rs1126179	−0.0022
	rs254363	−0.0245
	rs11642497	−0.0073
	rs1862667	0.0276
	rs1559307	−0.0004
	rs1080383	0.0198
	rs7198357	−0.0072
	rs7203768	−0.01
	rs4889197	−0.0026
	rs7194648	0.0013
	rs2866262	−0.0037
	rs7185108	−0.0074
	rs4528602	0.0079
	rs17688919	0.0182
	rs11646710	0.0107
	rs12917864	−0.0095
	rs9925569	0.0171
	rs7197961	0.0198
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	rs11649462	0.0205
	rs2967321	0.0124
	rs11647182	0.0181
	rs1424165	0.0106
	rs2042434	−0.0039
	rs1035537	−0.0055
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	rs427976	0.0299
	rs3751797	0.0153
	rs4843404	0.0236
	rs4843405	0.0168
	rs8043893	0.0121
	rs8058785	−0.0036
	rs4378618	−0.0092
	rs7203610	−0.0018
	rs6540223	0.0264
	rs3826067	0.0273
	rs2360735	0.0067
	SNP_A-	−0.0373
	1832790
	rs2131432	−0.0183
	rs8072508	0.003
	SNP_A-	0.0032
	1833243
	rs2955821	0.0105
	rs230404	−0.0155
	rs150908	0.0194
	rs2567872	0.0032
	rs2567871	0.0008
	rs2325756	0.0132
	rs4262999	0.0192
	rs9908162	−0.0283
	rs7214863	0.0151
	rs940854	−0.0306
	rs4141200	0.0169
	rs4791362	−0.0246
	rs7210608	0.0029
	rs4791489	0.015
	rs4791495	−0.0291
	rs17560643	0.0305
	rs7212685	0.0223
	rs8068578	−0.0228
	rs10852820	−0.034
	rs1558267	0.0003
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	rs8080471	−0.0003
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	rs6502674	0.014
	rs8067882	−0.003
	rs8069593	0.0108
	rs12943420	0.016
	rs6505282	−0.0239
	rs16967400	0.0002
	rs7214958	0.0246
	rs121008	−0.0079
	rs11655913	−0.0011
	rs712046	0.024
	rs8074446	0.0276
	rs1109593	0.014
	rs8078723	−0.0404
	rs2302777	−0.005
	rs1405334	0.0348
	rs757274	−0.0063
	rs9941364	0.0149
	rs757102	0.0062
	rs17658000	−0.0081
	rs1378494	−0.0071
	rs4796784	0.0242
	rs7222316	−0.0072
	rs7209177	0.0033
	rs12601990	−0.0208
	rs11713	−0.0127
	rs4793035	−0.0117
	rs4792814	−0.0427
	rs1528072	−0.0232
	rs7221510	0.0344
	rs7405452	0.0334
	rs757554	−0.0187
	rs8064322	0.0115
	rs10515007	0.0232
	rs11079111	0.0015
	rs8072786	−0.0251
	rs7501602	−0.0204
	rs6504972	0.0181
	rs7220324	0.003
	rs4239197	0.0051
	rs8067664	0.0038
	rs8073334	−0.0158
	rs7209070	−0.0173
	rs2111016	0.0173
	rs870780	−0.0213
	rs2585845	0.0097
	rs7223491	0.0208
	rs6504178	0.0063
	rs2665795	−0.0018
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	rs17688272	−0.0189
	rs894582	0.0079
	rs12942924	−0.008
	rs8073268	0.0201
	rs4791051	−0.025
	rs7220127	0.0205
	rs7219499	0.0064
	rs9904266	0.006
	rs16959880	0.0048
	rs9910837	0.0268
	rs8073477	0.0116
	rs7210863	0.0021
	rs8067277	0.0019
	rs11652864	−0.0031
	rs7226175	0.0101
	rs1093994	−0.0257
	rs9911538	−0.0277
	rs11077442	−0.0239
	rs7216806	−0.0155
	rs1046896	0.0032
	rs2846650	0.002
	rs7241142	−0.0258
	rs9951259	0.0019
	rs568822	−0.0031
	rs7506221	−0.0178
	rs1658180	−0.0077
	rs8095810	−0.0158
	rs4797197	0.0098
	rs727616	0.0118
	rs17388230	−0.0005
	rs4798520	0.0143
	rs1016188	0.0121
	rs1002715	−0.007
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	rs619379	0.0168
	rs651568	−0.0137
	rs12606001	−0.0167
	rs4798749	0.0136
	rs633907	0.0133
	rs10502396	−0.0051
	rs1874768	0.0094
	rs7231366	−0.0333
	rs1284420	−0.0053
	rs786038	−0.0019
	rs786031	−0.0056
	rs9957219	0.0194
	rs7229053	0.0117
	rs2852741	0.027
	rs573845	−0.0172
	rs470463	0.0258
	rs16943923	0.0397
	rs11564410	0.0181
	rs9965582	−0.0071
	rs1692481	−0.0267
	rs356827	0.0096
	rs12964420	0.009
	rs1031732	−0.024
	rs10502582	−0.0262
	rs10502662	0.0132
	rs17651514	−0.0219
	rs8090179	−0.0167
	rs1905517	−0.0125
	rs1367837	−0.0021
	rs8096856	−0.002
	rs4362464	0.0179
	rs9950114	0.0244
	rs12953383	0.0184
	SNP_A-	0.02
	4201660
	rs11664621	0.0126
	rs9304346	0.0153
	rs299719	0.0142
	rs9952908	0.0382
	rs7235470	0.0164
	rs12955772	0.0201
	rs2953264	−0.0146
	rs2953261	0.0078
	rs2879415	−0.0038
	rs9962633	0.0206
	rs12960274	−0.0219
	rs16957436	−0.0276
	rs969047	0.0144
	rs1558536	−0.0132
	rs9959800	0.0145
	rs4801058	−0.0223
	rs8094838	−0.0236
	rs597741	0.0028
	rs3865419	0.0145
	rs4058288	0.015
	rs12454023	0.0118
	rs12457509	−0.0088
	rs3744865	−0.016
	rs2163107	0.0148
	rs11665249	0.0192
	rs4368253	0.0231
	rs650313	0.0117
	rs8099832	0.012
	rs8083437	0.0091
	rs1118433	0.007
	rs9961166	0.0153
	rs950408	−0.0124
	rs17665435	0.0325
	rs683109	0.0029
	rs3810031	0.0235
	rs2850761	−0.0012
	rs7227145	−0.0151
	rs1455557	0.0023
	rs17071220	−0.0228
	rs1863583	0.0042
	rs13370501	0.0112
	rs2715282	0.0198
	rs2333505	−0.0023
	rs8088426	−0.0116
	rs8088774	−0.016
	rs11151403	0.0074
	rs7226844	−0.0139
	rs12960357	0.0208
	rs8096331	−0.0164
	rs4426448	0.0455
	SNP_A-	0.0246
	1836916
	rs17082947	0.0204
	rs4243328	0.0161
	rs17225992	−0.0124
	rs1879682	0.0058
	rs2685441	−0.0292
	rs1942474	−0.0077
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	rs8094131	0.003
	rs8089151	0.0346
	rs7407812	0.0091
	rs11875775	0.0155
	rs690227	−0.0028
	rs10514217	−0.0081
	rs1717548	0.003
	rs1623269	−0.0139
	rs610806	−0.0139
	rs3809928	0.0028
	rs12606550	−0.001
	rs7260635	−0.0022
	rs415647	−0.0163
	rs2216662	0.0143
	rs1862462	0.0115
	rs2431795	0.0122
	rs3745597	−0.0288
	rs7256992	0.045
	rs12710152	0.011
	rs12982420	0.0263
	rs11673570	−0.0145
	rs11879734	0.0039
	rs12609496	−0.0071
	rs7249829	0.0199
	rs33439	0.0336
	rs3745785	−0.0104
	rs7258461	0.0134
	rs16967984	−0.016
	rs285686	0.0239
	rs10421478	−0.0208
	rs10414846	0.0202
	rs1402468	0.0144
	rs1620082	−0.0258
	rs7248248	0.0036
	rs11083515	0.0133
	rs2159324	0.0109
	rs4802322	−0.0064
	rs12461370	−0.0125
	rs2303690	−0.0106
	rs10404905	0.0248
	rs753307	0.0103
	rs10401904	0.0012
	rs10411879	−0.0189
	rs8105809	−0.0001
	rs9941465	−0.0313
	rs302838	0.0393
	rs8108715	−0.0146
	rs7252632	−0.0349
	rs10408146	−0.0114
	rs10417057	0.0111
	rs6118784	−0.005
	rs6133763	0.001
	rs2143541	−0.0059
	rs1161237	−0.0007
	rs1178015	−0.015
	rs6115865	−0.0338
	rs241601	0.0096
	rs6139317	−0.0085
	rs6052673	0.0034
	rs3746674	0.0328
	rs1343178	−0.0196
	rs990928	0.0206
	rs1569874	0.0103
	rs6056909	0.0215
	rs6074180	−0.018
	rs6134481	−0.0066
	rs6041229	−0.0169
	rs6074548	−0.0032
	rs6033574	−0.002
	rs8183260	−0.011
	rs6042376	−0.0378
	rs3932489	0.0212
	rs1225888	0.0114
	rs6080193	−0.0161
	rs2208766	−0.0211
	rs753213	0
	rs6075279	−0.0046
	rs16979106	0.0018
	rs1475126	0.0154
	rs6081489	0.0253
	rs6035224	0.0001
	rs1555271	0.0178
	rs199790	−0.0099
	rs199793	−0.012
	rs16984335	0.0107
	rs7353712	0.022
	rs2260455	0.0149
	rs6050403	−0.0307
	rs1543438	−0.0068
	rs6088619	0.0048
	rs6060669	0.0399
	rs8116328	−0.0164
	rs1205441	−0.0041
	rs1119803	−0.0166
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	rs1002154	−0.0049
	rs4810281	0.0086
	rs941797	0.0214
	rs4812566	0.0135
	rs2223542	0.0027
	rs8119733	−0.0035
	rs6103041	0.034
	rs1973949	−0.0078
	rs927058	0.0168
	rs459681	−0.0058
	rs6032544	0.0068
	rs376438	0.0057
	rs6019032	0.0119
	rs2426103	0.0223
	rs660613	0.0178
	rs4591400	0.0216
	rs11906291	0.0014
	rs230002	0.0132
	rs6021700	0.0133
	rs4809901	−0.0063
	rs6013463	−0.0121
	rs1403742	−0.0089
	rs2256452	−0.022
	rs6022784	0.0181
	rs4811614	0.0151
	rs6024333	0.0061
	rs6070149	−0.023
	rs6026574	−0.0104
	rs13037047	0.0134
	rs2427144	−0.0112
	rs2822524	0.003
	rs2822891	−0.0221
	rs2822892	−0.0191
	rs412399	0.0124
	rs420571	−0.0047
	rs12482230	0.0082
	rs2823806	0.0021
	rs414663	−0.016
	rs11088618	−0.0186
	rs2776064	−0.0287
	rs17777477	−0.0173
	rs1079827	0.0015
	SNP_A-	−0.002
	1969384
	rs9981165	0.0297
	rs2827528	−0.0248
	rs2827915	0.0091
	rs1506009	−0.0251
	rs2262265	0.0002
	rs1892728	−0.0353
	rs465318	−0.009
	rs3017533	0.0204
	rs235930	−0.0109
	rs2156292	0.0151
	rs2830986	0.0113
	rs2831543	−0.0048
	rs383576	−0.0105
	rs363487	−0.025
	rs1016700	−0.009
	rs2832445	−0.0201
	rs2252898	−0.0045
	rs2833423	−0.0116
	rs2833538	−0.015
	rs8134837	−0.019
	rs2012993	0.0082
	rs2833654	−0.0379
	rs933122	−0.0011
	rs2070359	−0.0148
	rs7280071	0.011
	rs4817889	0.0221
	rs2836482	0.0157
	rs1022423	−0.0266
	rs1235555	0.0333
	rs7282356	0.015
	rs746063	0.0269
	rs2838008	0.0088
	rs9305744	0.0091
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	rs8132937	−0.0077
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	rs3827281	−0.0002
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	rs2800973	0.0116
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	rs9607337	−0.0335
	rs131838	−0.0108
	rs2284060	0.008
	rs738477	0.0066
	rs738479	−0.0248
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	rs5765532	−0.0332
	rs5768257	0.0205
	rs5766999	0.0136
	rs5770018	−0.0107

All publications mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the described methods and system of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in genetics, diagnostic assays, molecular biology or related fields are intended to be within the scope of the following claims.